[02322] Low discrepancy point sets inspired by Sudoku hypercubes
Session Time & Room : 3E (Aug.23, 17:40-19:20) @G601
Type : Contributed Talk
Abstract : Monte Carlo methods are effective to avoid the "Curse of Dimensionality," while not perfect since their convergences are late.
To overcome the weakness, quasi-Monte Carlo methods have been developed.
Some of the methods use low discrepancy point sets called $(T, M, S)$-nets.
In this talk, I present a new construction procedure of $(T, M, S)$-nets from orthogonal arrays as an application of the extension of Sudoku to higher dimensions named Sudoku hypercubes.
